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How to Help Community-Based mHealth Programs Collect High-Quality Data

By Guest Writer on February 20, 2017

As health programs are relying more on decentralized models of care, mHealth has made it easier to collect, manage and store community-level health data. This data can feed into national health information systems and be used to inform decision making for improved service delivery at the community level. But how can we ensure that our community-based mHealth programs are collecting accurate, high-quality data that will help us deliver the right services to the right places at the right time?

We at MEASURE Evaluation have developed the mobile community based health information system (CBHIS) data quality assessment toolkit to allow programs and projects to rapidly assess the ability of their mobile data systems to collect, manage, and report high-quality community-based data. We recently had the opportunity to present our toolkit for the first time to a group of participants at the MERL Tech Conference.

Our toolkit consists of a set of guidelines along with related assessment items that are housed in a Microsoft Excel workbook. The toolkit covers four main themes:

  • Designing mobile data forms and platforms to ensure quality data. Building in components such as skip logics, automated calculations, and data validations in your data collection workflow can increase the accuracy and completeness of data.
  • Empowering health workers and their supervisors to build accountability and ownership for data quality. We recognize that humans are at the crux of data collection and supportive feedback and motivation is required to engender high data quality.
  • Ensuring a functional data management and reporting system
  • Verifying the validity of data collected via a mobile device through a community trace and verify process

Themes 1-3 have an assessment component in which assessment items are scored as 1 (No, not at all), 2 (Partly), and 3 (Yes, completely). To complete the assessment, a team will conduct office and community visits with mobile CBHIS program implementers to carry out document reviews and interviews. The assessors will observe the implementation and use of mobile CBHIS, and interview community-based data collectors and key informants (such as mobile application developers, field engineers, mHealth specialists, and M&E officers) to answer relevant assessment statements.

These responses can be entered directly into a Microsoft Excel workbook, where a dashboard with graphic summaries of scores is generated. Programs can use these scores to identify areas of strengths and weaknesses and come up with an action plan to address any issues in their system, monitoring progress over time.

At the conference, participants were divided into small groups that gave us feedback on the toolkit and suggestions for improvement. Across the board, participants acknowledged the need for this type of assessment and discussed their experiences with the quality of data collected with mobile devices in their programs.

One key theme that emerged was that developers and implementers needed to work closely together from the outset to ensure that data collected in a mobile system was relevant and useful for program improvement. They also shared innovative practices currently being used by programs to ensure good supervision and high data quality.

For example, mobile devices can randomly record a few seconds of each community health worker visit to verify that visits are taking place and prevent the falsification of data.

We are currently revising the toolkit based on feedback from the MERL Tech breakout session and feedback from our core group of stakeholders. We plan to pilot the tool via community-based health programs before the end of the year.

Stay tuned to the MEASURE Evaluation website, where, in 2017, we will publish the final toolkit and assessment. If you would like more information, are interested in piloting the toolkit, or would like to be added to the stakeholder group that we are tapping for feedback during the toolkit’s development, please contact Jennifer Burnett-Zieman.

By Michelle Li, MEASURE Evaluation

Filed Under: Healthcare
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